{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train: 944\n",
      "test: 202\n",
      "val: 202\n",
      "For K = 50:\tSVM 0.866337\tAdaBoost 0.846535\tK-Means Inertia 35687298176.183937\n",
      "For K = 150:\tSVM 0.851485\tAdaBoost 0.821782\tK-Means Inertia 32011635638.862656\n",
      "For K = 300:\tSVM 0.856436\tAdaBoost 0.821782\tK-Means Inertia 29823263304.138237\n",
      "For K = 500:\tSVM 0.891089\tAdaBoost 0.861386\tK-Means Inertia 28436432214.062637\n"
     ]
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "# use results from panda_detector_K_gridsearch.py\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.externals import joblib\n",
    "from sklearn import metrics\n",
    "import numpy as np\n",
    "\n",
    "# Load from pickles generated in panda_detector_K_gridsearch.py\n",
    "results = joblib.load('pickles/k_grid_result/result.pickle')\n",
    "\n",
    "for obj_name in ['X_train', 'X_test', 'X_val', 'y_train', 'y_test', 'y_val']:\n",
    "    exec(\"{obj_name} = joblib.load('pickles/k_grid_feature_data/{obj_name}.pickle')\".format(obj_name=obj_name))\n",
    "\n",
    "print 'train:', len(X_train)\n",
    "print 'test:', len(X_test)\n",
    "print 'val:', len(X_val)\n",
    "    \n",
    "\n",
    "K_vals = sorted(results.keys())\n",
    "for K in K_vals:\n",
    "    print 'For K = %i:\\tSVM %f\\tAdaBoost %f\\tK-Means Inertia %f' % (\n",
    "        K, results[K]['svm_score'], results[K]['ada_score'], results[K]['inertia']);\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SVC(C=10, cache_size=200, class_weight=None, coef0=0.0,\n",
      "  decision_function_shape=None, degree=3, gamma=0.0001, kernel='rbf',\n",
      "  max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
      "  tol=0.001, verbose=False)\n"
     ]
    }
   ],
   "source": [
    "# right now my best is the SVM as K=150\n",
    "\n",
    "svc = results[500]['svmGS']\n",
    "print svc.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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ll13Gvn37vA4l6ixBBBFs6AwbRsOY2mHXrl0MHTqUP/zhDwwaNIj69et7HVLUWYIIwvci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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6069a7f250>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_score = svc.decision_function(X_test) #dist of samples to the boundary hyperplane\n",
    "fpr, tpr, thresholds = metrics.roc_curve(y_test, y_score, pos_label='True')\n",
    "roc_auc = metrics.auc(fpr, tpr)\n",
    "\n",
    "# Plot of a ROC curve for a specific class\n",
    "plt.figure()\n",
    "plt.plot(fpr, tpr, label='ROC curve (area = %0.2f)' % roc_auc)\n",
    "plt.plot([0, 1], [0, 1], 'k--')\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.ylim([0.0, 1.05])\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('Receiver operating characteristic example')\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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VyD1QM7MSeT1QM7MSuQdqZlYiL6hsZlYiD+HNzErkIbyZWYl8GZOZWYncAzUz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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f60695cb6d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "target_labels = ['True','False']\n",
    "target_names = ['panda','not panda']\n",
    "\n",
    "y_pred = svc.predict(X_test)\n",
    "cm = metrics.confusion_matrix(y_test, y_pred, labels=target_labels)\n",
    "cm_normalized = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n",
    "\n",
    "def plot_confusion_matrix(cm, title='Confusion matrix', cmap=plt.cm.Blues):\n",
    "    plt.imshow(cm, interpolation='nearest', cmap=cmap)\n",
    "    plt.title(title)\n",
    "    plt.colorbar()\n",
    "    tick_marks = np.arange(len(target_names))\n",
    "    plt.xticks(tick_marks, target_names, rotation=45)\n",
    "    plt.yticks(tick_marks, target_names)    \n",
    "    plt.tight_layout()\n",
    "    plt.ylabel('True label')\n",
    "    plt.xlabel('Predicted label')\n",
    "    for i, cas in enumerate(cm):\n",
    "        for j, c in enumerate(cas):\n",
    "            # formatting for ints vs floats\n",
    "            if isinstance(c, int):\n",
    "                plt.text(j-.2, i+.2, c, fontsize=14)\n",
    "            else:\n",
    "                plt.text(j-.2, i+.2, '%.2f' % c, fontsize=14)\n",
    "\n",
    "plot_confusion_matrix(cm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Ur+aUzp1SfwKOBr4FMLOJwIFNWSjnnEtHPj4CJWZmcxI6fyubqDzOOZe2vBmx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\n5zLk/wHsl/Fazrp15gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6064d27050>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# rows add up to 1.0\n",
    "plot_confusion_matrix(cm_normalized, title='Normalized confusion matrix (rows add to 1.0)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "      panda       0.85      0.92      0.89        92\n",
      "  not panda       0.93      0.86      0.90       110\n",
      "\n",
      "avg / total       0.89      0.89      0.89       202\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print metrics.classification_report(y_test, y_pred, labels=target_labels, target_names=target_names)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# WARNING: This is all for test data. For the final analysis you want to use the final validation set (which has been untouched so far) instead."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
